DaedalMap World Development Indicators

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the agent knows it's safe. Description adds 'free discovery' and 'live agent-ready data packs', which is minor behavioral context. No contradictions. With annotations covering the safety profile, description adds limited extra behavioral detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two short phrases that front-load the purpose. Every word carries meaning. No unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 0 parameters and no output schema, the description is minimally adequate. It lacks details about the format of the returned list (e.g., pack names, metadata). However, the tool is simple and the purpose is clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 0 parameters and schema coverage is 100% (trivially). Per guidelines, baseline is 4 for 0 parameters. Description does not need to explain parameters, as there are none.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool returns a list of live agent-ready data packs from DaedalMap. It is specific about the resource (data packs) and the source. While it distinguishes from siblings implicitly (get_pack likely gets one pack, query_dataset queries data), it could explicitly differentiate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Free discovery' implies usage for initial exploration, but no explicit guidance on when to use this tool versus siblings (get_pack, query_dataset). No when-not or alternatives mentioned. For a simple listing tool, this is acceptable but not exemplary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, and description adds 'Free discovery' and mentions 'paste-ready first query', enhancing understanding of behavior. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each with clear purpose: first states what it does, second gives usage context. No waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers what the tool does and when to use it for a simple one-parameter tool without output schema. Complete and sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the parameter description is clear. The tool description adds no additional parameter details beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the verb 'Returns' and lists the specific metadata returned (metadata, coverage, etc.). It distinguishes from siblings by implying it is a precursor to querying, but doesn't explicitly contrast with get_catalog or query_dataset.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises to call this before querying a new pack, providing clear when-to-use guidance. Does not mention exclusions or alternatives, but the context with siblings is implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the agent knows it's a safe read operation. The description adds that it uses the same contract as the API, which is useful but does not disclose other behavioral traits like error handling or response format. With annotations covering safety, a score of 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that is front-loaded with the purpose and then lists packs. It is relatively concise, though the long list of packs could be formatted as a bullet for readability. Overall, it is efficiently worded without unnecessary fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 8 parameters with nested objects and no output schema, yet the description does not explain the return format, how filters/metrics work together, or behavior when both source_id and pack_id are provided. This leaves significant gaps for a complex query tool. Adding more behavioral context would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 8 parameters. The description adds value by listing free and paid packs, which clarifies the pack_id parameter, but does not elaborate on other parameters beyond what the schema provides. This meets the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it's a 'Generic structured query for direct source_id or pack_id access' using a specific API contract. This effectively distinguishes it from sibling tools get_catalog and get_pack, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It lists free and paid packs, providing some context on when to use each pack, but does not explicitly state when to use this tool versus alternatives or provide exclusions. The guidance is implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.